input_size

app.py

@@ -172,3 +172,5 @@ demo = gr.Interface(
 
 demo.launch(share=True, allowed_paths=["."], ssr_mode=False)
 
+
+

llama_diffusion_model.py

@@ -213,6 +213,8 @@ class CustomTransformerModel(PreTrainedModel):
         self.llama = get_peft_model(self.llama, lora_config)
         self.llama.print_trainable_parameters()  # Print number of trainable parameters
         self.llama = self.llama.to(torch.float16)
+        self.input_size = 256
+
 
     def forward(self, input_ids, labels=None, **kwargs):
         batch_size, seq_length = input_ids.shape